String transformations may be useful in correcting a spelling error, generating alternate queries, and reformulating queries. However, often the techniques used to generate candidate string transformations are either accurate or efficient, but not both. Often an approach focuses on, and provides, one at the expense of the other.
For example, Hadjieleftheriou discusses employing machine learning to generate an accurate transformation model over efficiency. Hadjieleftheriou and Li, “Efficient approximate search on string collections,” Proc. VLDB Endow., vol. 2, pp. 1660-1661, August 2009. In comparison, Yang discusses using efficient data structures with a fixed similarity model limiting accuracy. Yang, Yu, and Kitsuregawa, “Fast algorithms for top-k approximate string matching,” in Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence, ser. AAAI '10, 2010, pp. 1467-1473.
This may cause problems for the user or the application interested in fast and accurate string transformations.